Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- multi_nli
+language:
+- en
+pipeline_tag: text-classification
+---
+
+# DeBERTa-v3 (large) fine-tuned to Multi-NLI (MNLI)  
+This model is for Textual Entailment (aka NLI), i.e., predict whether `textA` is supported by `textB`. More specifically, it's a 2-way classification where the relationship between `textA` and `textB` (A -> B) can be **entail, neutral, contradict**.  
+
+- Input: (`textA`, `textB`)
+- Output: prob(entail), prob(contradict)
+
+Note that during training, all 3 labels (entail, neural, contradict) were used. But for this model, the neural output head has been removed.
+
+## Model Details
+- Base model: [deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large)
+- Training data: [MNLI](https://huggingface.co/datasets/multi_nli)
+- Training details: num_epochs = 3, batch_size = 16, `textA=hypothesis`, `textB=premise`
+
+## Example
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+tokenizer = AutoTokenizer.from_pretrained("potsawee/deberta-v3-large-mnli")
+model = AutoModelForSequenceClassification.from_pretrained("potsawee/deberta-v3-large-mnli")
+
+textA = "Kyle Walker has a personal issue"
+textB = "Kyle Walker will remain Manchester City captain following reports about his private life, says boss Pep Guardiola."
+
+inputs = tokenizer.batch_encode_plus(
+    batch_text_or_text_pairs=[(textA, textB)],
+    add_special_tokens=True, return_tensors="pt",
+)
+logits = model(**inputs).logits # neutral is already removed
+probs = torch.softmax(logits, dim=-1)[0]
+# probs = [0.7080, 0.2920], meaning that prob(entail) = 0.708, prob(contradict) = 0.292
+```
+
+## Citation
+
+```bibtex
+@article{manakul2023selfcheckgpt,
+  title={Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models},
+  author={Manakul, Potsawee and Liusie, Adian and Gales, Mark JF},
+  journal={arXiv preprint arXiv:2303.08896},
+  year={2023}
+}
+```